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Data Assimilation and Retrieval Theory

Official description

Data assimilation involves combining observations with model output to obtain a consistent, evolving 3-dimensional picture of the atmosphere. This process is used to generate an initial state for producing forecasts at operational weather forecast centers. Data assimilation can also provide added value to observations by filling in data gaps and inferring information about unobserved variables. In this course, common methods of data assimilation (optimal  interpolation, Kalman filtering, variational methods) are introduced and derived in the context of estimation theory. A hands-on approach will be taken so that methods introduced in the lectures will be implemented in computer assignments using toy models.

Additional information


1. Swinbank, R., V. Shutyaev, and W.A. Lahoz, 2003: "Data Assimilation for the Earth System," Kluwer Academic Publishers.

2. Daley, R., 1991: "Atmospheric Data Analysis," Cambridge University Press.

3. Rodgers, C., 2000: "Inverse Methods for Atmospheric Sounding," World Scientific Publishing.

4. Todling, R., 1999: "Estimation Theory and Foundations of Atmospheric Data Assimilation," DAO Office Note 1990-01.


Computer assignments 50%, project 50%.

course title
specialized course
time and location
Time: R 10-12

Delivery Methods

In Person

A course is considered In Person if it requires attendance at a specific location and time for some or all course activities.*.

* Subject to adjustments imposed by public health requirements for physical distancing.

Online - Synchronous
A course is considered Online Synchronous if online attendance is expected at a specific time for some or all course activities, and attendance at a specific location is not expected for any activities or exams.
A course is considered Asynchronous if it has no requirement for attendance at a specific time or location for any activities or exams.